Pattern separation and integration in hippocampus are the result of memory reactivation during learning

Efficient learning requires encoding of both detailed information from individual events and generalities across experiences. In service of these goals, it is hypothesized that hippocampus can create orthogonal memories through pattern separation or form integrated representations that code common features across memories. To optimize learning, it is critical to identify how these neural codes come to coexist within hippocampus. One factor that may influence whether related memories become integrated or separated is memory reactivation during learning. Recent evidence indicates that memories are reactivated in neocortex when learning overlaps with past experience. Although reactivation is thought to promote integration of new information into existing memories, reactivation may also lead to competition between memories, requiring pattern separation. Here, we used fMRI to test the hypothesis that the strength of reactivation during learning influences whether hippocampus separates or integrates related memories. Participants learned initial picture associations (AB pairs) and overlapping associations (BC pairs). Before and after learning, participants viewed individual images (A and C). We used pattern analysis to assess whether hippocampal activation patterns for indirectly related images (A and C) became less similar (separated) or more similar (integrated) after learning. Additionally, we used pattern classification to measure reactivation of related memories (A items) in ventral temporal cortex during overlapping event (BC) learning. Consistent with our hypothesis, we found that the strength of memory reactivation predicted whether indirectly related memories were separated or integrated in hippocampal subfields. These findings provide insight into the basis of the different neural codes formed by hippocampus.